JpGU-AGU Joint Meeting 2020

Presentation information

[E] Oral

M (Multidisciplinary and Interdisciplinary) » M-TT Technology & Techniques


convener:Yuichi S. Hayakawa(Faculty of Environmental Earth Science, Hokkaido University), Shigekazu Kusumoto(Graduate School of Science and Engineering for Research, University of Toyama), Christopher A Gomez(Kobe University Faculty of Maritime Sciences Volcanic Risk at Sea Research Group)

[MTT49-03] Comparison of LiDAR data using three platforms (Aerial-LiDAR, UAV-LiDAR, Terrestrial-LiDAR) in evergreen coniferous forest

*Ebina Masuto1, Akira Kato2, Fumio Takeuchi1, Shouichi Kondou3 (1.Forestry Research Institute, Hokkaido Research Organization, 2.Graduate School of Horticulture, Chiba University, 3.Industrial Research Institute, Hokkaido Research Organization)

Keywords:LiDAR, Evergreen coniferous forest, Terrain, Tree height

LIDAR based forest measurement becomes popular in forest inventory field. LIDAR provides three-dimensional data of terrain and vegetation by aerial and terrestrial platforms. The data has been used to estimate amount of biomass from above ground data and design forest roads from terrain data created from ground data. In this study, we compare three different platforms LiDAR data acquired over evergreen coniferous forest in Urahorocho, Hokkaido. The three platforms are airborne-, UAV-, and terrestrial-LiDAR. The tree height measurement was influenced by the data quality of terrain and canopy surface model created from the three different platforms. The result shows the differences reflected by different point density of terrain and vegetation. The UAV-LIDAR provides high quality point density and gives more solid model improves an accuracy for tree species classification using deep learning and reduces computational cost to estimate accurate tree height measurement. The higher point density from UAV has potential to improve data collection efficiency of forest inventory.